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Prescriptive Analytics Vs. Artificial Intelligence

Analytics have grown in complexity to meet business demand. 2018 is seeing the next wave of advanced analytics – prescriptive analytics – which leverage historical data to identify what could happen and go a step further by recommending what should be done to give rise to a desired outcome. They use knowledge not only to make better decisions in the future, but also to offer insights on the best course of action for a particular situation given what the future is likely to hold.

The relatively new kid on the block, A.I., is dedicated to automating “intelligent processes” that imitate human logic, and is incorporated by nearly all businesses in at least some way – from recommendation systems to chatbots, fraud prevention, and even A.I. autopilot on commercial flights. A.I. can build and rebuild models on the fly while adapting to change and searching for new and better data – making it a proactive solution.

Prescriptive analytics alone are great at making predictions from an expansive data set and generating a best course of action, but if their insights are wrong they don’t have the capacity to understand and fix the underlying models. This would have to wait for a guiding hand to feed the models new data and rework their underlying assumptions and retune them to the correct answer.

Can the two come together?

By combining the models of prescriptive analytics with the autonomy and agility of A.I., businesses have access to a sidekick that can go beyond predicting the future to actually making it happen. As an avalanche of structured and unstructured data pours into prescriptive models, real-time decisioning via A.I. can turn into real-time action, without depending on human intervention, and bring your automated businesses processes to life.